Snowflake Lead Data Engineering

Must Have Technical/Functional Skills

Snowflake, Cortex AI, Python and Insurance Domain on AWS Cloud, Talend ETL

Roles & Responsibilities

The Snowflake Lead will serve as the onsite technical lead for CLEARBROOK ’s enterprise data platform, responsible for end to end Snowflake solution design, development leadership, and AI/advanced analytics enablement. The role requires deep hands on expertise in Snowflake, strong data engineering fundamentals, and the ability to integrate AI/ML driven use cases into the Snowflake ecosystem while coordinating with offshore teams.

 

Snowflake Production Support

• Perform root cause analysis for job failures and data analysis & fixes 

• Perform Month End Closing Activities

Snowflake Development & Architecture

• Lead design and development of Snowflake schemas, tables, views, streams, tasks, and Snowpipes

• Define and enforce best practices for performance optimization (warehouse sizing, clustering, query tuning)

• Own Snowflake security architecture: RBAC, role hierarchy, data masking, row/column level security

• Oversee promotion of code across environments using CI/CD practices 

Data Engineering & Integration

• Lead development of batch and near real time ingestion pipelines using Talend / Qlik Replicate / Snowpipe

• Ensure data quality checks, reconciliation, and schema drift handling

• Guide integration from insurance source systems (Policy, Claims, Billing, Reinsurance) into Snowflake

• Provide technical oversight for SQL, Python, and ELT based transformations

AI / Advanced Analytics Enablement

• Enable AI/ML use cases on Snowflake, including: 

o Feature engineering datasets for ML models

o Snowpark (Python)–based data processing

o Integration with external ML platforms (Databricks / SageMaker / Azure ML where applicable)

• Support AI driven insights such as: 

o Claims triage & risk scoring

o Fraud detection inputs

o Premium leakage and pricing analytics

• Guide teams in using Python, SQL, and Snowflake native capabilities for data science workloads

 

Core Technical Skills

• Snowflake: Advanced SQL, Performance Tuning, Security, Snowpipe, Streams & Tasks

• Data Engineering: ELT/ETL patterns, data modeling, CDC concepts

• Python: Data processing, automation, Snowpark (preferred)

• Strong understanding of cloud data platform architecture (AWS preferred)

 

AI / Analytics Skills

• Hands on exposure to AI/ML pipelines (feature preparation, training data creation)

• Experience supporting ML models through data engineering and operationalization

• Familiarity with Python ML libraries (scikit learn, pandas, NumPy) – applied from a data engineering perspective

• Understanding of model lifecycle support (data refresh, monitoring, retraining inputs)

 

Domain & Soft Skills

• Insurance domain experience (P&C / Specialty Insurance strongly preferred)

• Strong communication skills for onsite customer interaction

• Ability to translate business requirements into scalable data & AI solutions